Real-time monitoring of cellular oxidative stress during aerosol sampling: a proof of concept study.
Ontology highlight
ABSTRACT:
SUBMITTER: Secondo LE
PROVIDER: S-EPMC7736142 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:A co-engineered nanoparticle/protein peroxide detector is created. This system features a gold nanoparticle functionalized with a galactose headgroup (AuNP-Gal) that reacts covalently with a boronate-modified green fluorescent protein (PB-GFP). Boronate acid-saccharide complexation between PB-GFP and AuNP-Gal affords a highly stable assembly. This complex is disrupted by peroxide, allowing quantitative and selective monitoring of hydrogen peroxide production in real time.
| S-EPMC5101542 | biostudies-literature
Project description:Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low- to middle-income countries varies from partial coverage to essentially nothing at all. Consequently the state of the art for public health information in low- to middle-income countries is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space - data amalgamation. Data sources amenable to this approach include sample surveys, sample registration systems, health and demographic surveillance systems, administrative records, census records, health facility records and others. We propose a new statistical framework for gathering health and population data - Hyak - that leverages the benefits of sampling and longitudinal, prospective surveillance to create a cheap, accurate, sustainable monitoring platform. Hyak has three fundamental components: Data amalgamation: A sampling and surveillance component that organizes two or more data collection systems to work together: (1) data from HDSS with frequent, intense, linked, prospective follow-up and (2) data from sample surveys conducted in large areas surrounding the Health and Demographic Surveillance System (HDSS) sites using informed sampling so as to capture as many events as possible;Cause of death: Verbal autopsy to characterize the distribution of deaths by cause at the population level; andSocioeconomic status (SES): Measurement of SES in order to characterize poverty and wealth. We conduct a simulation study of the informed sampling component of Hyak based on the Agincourt HDSS site in South Africa. Compared with traditional cluster sampling, Hyak's informed sampling captures more deaths, and when combined with an estimation model that includes spatial smoothing, produces estimates of both mortality counts and mortality rates that have lower variance and small bias.
| S-EPMC5870438 | biostudies-literature
Project description: Not available
| S-EPMC8166515 | biostudies-literature
Project description:ObjectiveThe feasibility of a high-frequency real-time monitoring approach to psychotherapy is outlined and tested for patients' compliance to evaluate its integration to everyday practice. Criteria concern the ecological momentary assessment, the assessment of therapy-related cognitions and emotions, equidistant time sampling, real-time nonlinear time series analysis, continuous participative process control by client and therapist, and the application of idiographic (person-specific) surveys.MethodsThe process-outcome monitoring is technically realized by an internet-based device for data collection and data analysis, the Synergetic Navigation System. Its feasibility is documented by a compliance study on 151 clients treated in an inpatient and a day-treatment clinic.ResultsWe found high compliance rates (mean: 78.3%, median: 89.4%) amongst the respondents, independent of the severity of symptoms or the degree of impairment. Compared to other diagnoses, the compliance rate was lower in the group diagnosed with personality disorders.ConclusionThe results support the feasibility of high-frequency monitoring in routine psychotherapy settings. Daily collection of psychological surveys allows for the assessment of highly resolved, equidistant time series data which gives insight into the nonlinear qualities of therapeutic change processes (e.g., pattern transitions, critical instabilities).
| S-EPMC4853656 | biostudies-literature
Project description:Knowledge of the airborne nature of respiratory disease transmission owes much to the pioneering experiments of Wells and Riley over half a century ago. However, the mechanical, physiological, and immunopathological processes which drive the production of infectious aerosols by a diseased host remain poorly understood. Similarly, very little is known about the specific physiological, metabolic and morphological adaptations which enable pathogens such as Mycobacterium tuberculosis (Mtb) to exit the infected host, survive exposure to the external environment during airborne carriage, and adopt a form that is able to enter the respiratory tract of a new host, avoiding innate immune and physical defenses to establish a nascent infection. As a first step towards addressing these fundamental knowledge gaps which are central to any efforts to interrupt disease transmission, we developed and characterized a small personal clean room comprising an array of sampling devices which enable isolation and representative sampling of airborne particles and organic matter from tuberculosis (TB) patients. The complete unit, termed the Respiratory Aerosol Sampling Chamber (RASC), is instrumented to provide real-time information about the particulate output of a single patient, and to capture samples via a suite of particulate impingers, impactors and filters. Applying the RASC in a clinical setting, we demonstrate that a combination of molecular and microbiological assays, as well as imaging by fluorescence and scanning electron microscopy, can be applied to investigate the identity, viability, and morphology of isolated aerosolized particles. Importantly, from a preliminary panel of active TB patients, we observed the real-time production of large numbers of airborne particles including Mtb, as confirmed by microbiological culture and polymerase chain reaction (PCR) genotyping. Moreover, direct imaging of captured samples revealed the presence of multiple rod-like Mtb organisms whose physical dimensions suggested the capacity for travel deep into the alveolar spaces of the human lung.
| S-EPMC4726558 | biostudies-literature
Project description:Reactive oxygen species (ROS) regulate various physiological and pathological conditions in cells by interacting with signaling molecules and inducing oxidative stress. Therefore, sensitive monitoring of ROS levels in living cells is important to track cellular state and study the complex role of ROS in the development of various pathologies. Herein, we present an optically tunable plasmonic interface covered with graphene to monitor cellular ROS levels with superior sensitivity and cellular comfortability. As a sensing principle, we employed plasmon resonance energy transfer (PRET)-based spectral quenching dips modulated by redox-active cytochrome c for real-time monitoring. By transferring graphene layers to plasmonic nanoparticles immobilized on a glass substrate, the scattering profiles of the nanoprobes were adjusted in terms of the position, width, and intensity of the peaks to determine the optimal conditions for measuring the PRET signal. Using the optimized graphene-covered plasmonic nanoprobe, we obtained calibration curves over a wide concentration range from femtomoles to millimoles for hydrogen peroxide based on the change in the PRET signal. Before monitoring cellular ROS, we confirmed that a high density of cells adhered well to the graphene-covered plasmonic interface by observing immunofluorescence images of the cytoskeleton of the immobilized cells. Finally, we monitored the real-time ROS generated by the cells under oxidative stress conditions by directly measuring the spectral changes of the probes around the cells. We believe that the proposed graphene-covered tunable plasmonic interface has versatile applicability for investigating cellular stress and disease progression by monitoring ROS levels under various cellular conditions.
| S-EPMC9127018 | biostudies-literature
Project description:Rationale & objectiveAcute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm.Study designProspective observational cohort study.Setting & participants2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers.ExposureClinical characteristics at the time of pre-AKI alert.OutcomeAKI within 24 hours of pre-AKI alert (AKI24).Analytical approachDescriptive statistics and univariable associations.ResultsAt enrollment, mean predicted probability of AKI24 was 19.1%; 18.9% of patients went on to develop AKI24. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI24 and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI24 vs 18% without), heart rate>100 beats/min (32% of patients with AKI24 vs 24% without), and oxygen saturation<92% (15% of patients with AKI24 vs 6% without) were all more common among those who developed AKI24. Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI24 vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI24 vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI24.LimitationsSingle-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation.ConclusionsA real-time AKI risk model was successfully integrated into the EHR.
| S-EPMC8667815 | biostudies-literature
Project description:Novel methods that stimulate neuroplasticity are increasingly being studied to treat neurological and psychiatric conditions. We sought to determine whether real-time fMRI neurofeedback training is feasible in Huntington's disease (HD), and assess any factors that contribute to its effectiveness. In this proof-of-concept study, we used this technique to train 10 patients with HD to volitionally regulate the activity of their supplementary motor area (SMA). We collected detailed behavioral and neuroimaging data before and after training to examine changes of brain function and structure, and cognitive and motor performance. We found that patients overall learned to increase activity of the target region during training with variable effects on cognitive and motor behavior. Improved cognitive and motor performance after training predicted increases in pre-SMA grey matter volume, fMRI activity in the left putamen, and increased SMA-left putamen functional connectivity. Although we did not directly target the putamen and corticostriatal connectivity during neurofeedback training, our results suggest that training the SMA can lead to regulation of associated networks with beneficial effects in behavior. We conclude that neurofeedback training can induce plasticity in patients with Huntington's disease despite the presence of neurodegeneration, and the effects of training a single region may engage other regions and circuits implicated in disease pathology.
| S-EPMC5838530 | biostudies-literature
Project description:BACKGROUND:The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants. METHODS:We used published literature to estimate the numbers of patients who might be eligible to receive treatments approved for other indications based on molecular profiles. We then estimated the sample size and number of variables that would be required for a successful system to detect exceptional responses with sufficient power. We generated an appropriately sized, simulated dataset (n = 5000) and used an established HE algorithm to detect exceptional responses and calculate total drug exposure, while the data remained encrypted. RESULTS:Our results demonstrated the feasibility of using an HE-based system to identify exceptional responders and perform calculations on patient data during a hypothetical 3-year study. Although homomorphically encrypted computations are time consuming, the required basic computations (i.e., addition) do not pose a critical bottleneck to the analysis. CONCLUSION:In this proof-of-concept study, based on simulated data, we demonstrate that identifying exceptional responders to targeted cancer treatments represents a valuable and feasible use case. Past solutions to either completely anonymize data or restrict access through stringent data use agreements have limited the utility of abundant and valuable data. Because of its privacy protections, we believe that an HE-based learning system for real-world cancer treatment would entice thousands more patients to voluntarily contribute data through participation in research studies beyond the currently available secondary data populated from hospital electronic health records and administrative claims. Forming collaborations between technical experts, physicians, patient advocates, payers, and researchers, and testing the system on existing RWD are critical next steps to making HE-based learning a reality in healthcare.
| S-EPMC6894133 | biostudies-literature
Project description:Rapid diagnosis is key to curtailing the Covid-19 pandemic. One path to such rapid diagnosis may rely on identifying volatile organic compounds (VOCs) emitted by the infected body, or in other words, identifying the smell of the infection. Consistent with this rationale, dogs can use their nose to identify Covid-19 patients. Given the scale of the pandemic, however, animal deployment is a challenging solution. In contrast, electronic noses (eNoses) are machines aimed at mimicking animal olfaction, and these can be deployed at scale. To test the hypothesis that SARS CoV-2 infection is associated with a body-odor detectable by an eNose, we placed a generic eNose in-line at a drive-through testing station. We applied a deep learning classifier to the eNose measurements, and achieved real-time detection of SARS CoV-2 infection at a level significantly better than chance, for both symptomatic and non-symptomatic participants. This proof of concept with a generic eNose implies that an optimized eNose may allow effective real-time diagnosis, which would provide for extensive relief in the Covid-19 pandemic.
| S-EPMC8172018 | biostudies-literature